import sys
sys.path.extend(["..","../networks","../generators","../simulations"])
from moose_utils import * # imports moose, and 'moose.utils import *'
from networkConstants import *
from stimuliConstants import *
from data_utils import *
def setupTables(network, nopgs, nosingles, nojoints, nomultis, args={}, spikes=False):
pgtables = []
singletables = []
jointtables = []
multitables = []
## if spikes == True i.e. only spiketimes are recorded,
## then record all cells, else less number of cells.
allcells = spikes
if not nopgs:
## Setup the tables to pull data from PGs
## get cells connected to mitrals specified in args. if not specified get arbitrary.
PGlist = getCellsByMitralConnection(args, network, 'PG_mitral', 'PGs', allcells)
for i,PG in enumerate(PGlist):
# assumes at least one soma and takes the first!
PG.soma = moose.Compartment(get_matching_children(PG, ['Soma','soma'])[0])
PG._vmTableSoma = setupTable("vmTableSoma",PG.soma,'Vm')
pgtables.append(PG._vmTableSoma)
if spikes:
PG._vmTableSoma.stepMode = TAB_SPIKE
PG._vmTableSoma.stepSize = THRESHOLD
if not nosingles:
## Setup the tables to pull data from single granules
## get cells connected to mitrals specified in args. if not specified get arbitrary.
singlesList = getCellsByMitralConnection(args, network,
'granule_mitral_inh_singles', 'granules_singles', allcells)
for i,gran in enumerate(singlesList):
## assumes at least one soma and takes the first!
gran.soma = moose.Compartment(get_matching_children(gran, ['Soma','soma'])[0])
gran._vmTableSoma = setupTable("vmTableSoma",gran.soma,'Vm')
singletables.append(gran._vmTableSoma)
if spikes:
gran._vmTableSoma.stepMode = TAB_SPIKE
gran._vmTableSoma.stepSize = THRESHOLD
if not nojoints:
## Setup the tables to pull data from joint granules
## get cells connected to mitrals specified in args.
## if not specified get arbitrary.
jointsList = getCellsByMitralConnection(args, network,
'granule_mitral_inh_joints', 'granules_joints', allcells)
for i,gran in enumerate(jointsList):
## assumes at least one soma and takes the first!
gran.soma = moose.Compartment(get_matching_children(gran, ['Soma','soma'])[0])
gran._vmTableSoma = setupTable("vmTableSoma",gran.soma,'Vm')
jointtables.append(gran._vmTableSoma)
if spikes:
gran._vmTableSoma.stepMode = TAB_SPIKE
gran._vmTableSoma.stepSize = THRESHOLD
if not nomultis:
## Setup the tables to pull data from multi granules
## get cells connected to mitrals specified in args.
## if not specified get arbitrary.
multisList = getCellsByMitralConnection(args, network,
'granule_mitral_inh_multis', 'granules_multis', allcells)
for i,gran in enumerate(multisList):
## assumes at least one soma and takes the first!
gran.soma = moose.Compartment(get_matching_children(gran, ['Soma','soma'])[0])
gran._vmTableSoma = setupTable("vmTableSoma",gran.soma,'Vm')
multitables.append(gran._vmTableSoma)
if spikes:
gran._vmTableSoma.stepMode = TAB_SPIKE
gran._vmTableSoma.stepSize = THRESHOLD
return (pgtables, singletables, jointtables, multitables)
def exportTable(network, neuronProj, neuronPop, colours, \
args={}, spikes=True, allcells=True):
""" Return tables of cells in neuronPop population name
connected to mitrals specified in args,
via neuronProj projection name
if args is not specified get all.
NOTE: All tables for all cells' somas should be present if allcells is True.
If allcells is False, cells called here should have tables present.
"""
exportDict = {'spikes':spikes,'data_tables':[]}
if array(colours).shape == (3,): coloursList = False
else: coloursList = True
## get cells connected to mitrals specified in args. if not specified get arbitrary.
celllist = getCellsByMitralConnection(args, network, neuronProj, neuronPop, allcells)
for i,cell in enumerate(celllist):
cellname = cell.path.split('/')[-1]
## assumes at least one soma and takes the first!
cell.soma = moose.Compartment(get_matching_children(cell,['Soma','soma'])[0])
cellTablePath = cell.soma.path+"/data/vmTableSoma"
if moose.context.exists(cellTablePath):
cell._vmTableSoma = moose.Table(cellTablePath)
else:
print "SimError: Did not find "+cellTablePath
sys.exit(1)
if coloursList: colour=colours[i]
else: colour=colours
exportDict['data_tables'].append((cellname,colour,array(cell._vmTableSoma)))
return exportDict
def exportTables(network, nopgs, nosingles, nojoints, nomultis, mitspikes, args, colours):
## mitrals first each with a different colour
allTables = [ exportTable(network, '', 'mitrals', colours, \
args=args, spikes=mitspikes, allcells=True) ]
## Each colour-entry below is a tuple of (baseline/initial colour, spiking/peak colour, colourmap)
## Each colour is a tuple of (r,g,b,a)
## Set in Moogli's config file, whether to change color, and/or alpha, or use colourmap.
if not nopgs:
allTables.append( exportTable(network,\
'PG_mitral', 'PGs', ((0.3,0,0,0.3),(1,0,0,1),'jet'), args) )
if not nosingles:
allTables.append( exportTable(network,\
'granule_mitral_inh_singles', 'granules_singles', \
((0.3,0,0.3,0.3),(1,0,1,1),'jet'), args) )
if not nojoints:
allTables.append( exportTable(network,\
'granule_mitral_inh_joints', 'granules_joints', \
((0.3,0.3,0,0.3),(1,1,0,1),'jet'), args) )
if not nomultis:
allTables.append( exportTable(network,\
'granule_mitral_inh_multis', 'granules_multis', \
((0,0.3,0.3,0.3),(0,1,1,1),'jet'), args) )
return allTables
def getCellsByMitralConnection(args, network, projection, population, allcells=False):
"""
Returns a list of pre-synaptic MOOSE Cell-s in 'projection' that are connected to mitrals
specificied in args={'mitrals':[mitid1, mitid2, ...]}.
If args does not have 'mitrals' key, an arbitrary list of cells from 'population' are returned.
if allcells is False, a few cells are returned, else all cells are returned.
"""
cellList = []
cellUniques = []
if args.has_key('mitrals'):
for mitid in args['mitrals']:
mitpath = 'mitrals_'+str(mitid)
cellnum = 0
if projection in network.projectionDict:
for conn in network.projectionDict[projection][2]:
if mitpath in conn[2]: # if mitrals_<mitid> is substring of post_seg_path of this connection
cellpath = string.split(conn[1],'/')[1] # take out cellname from '/cellname/segmentname
## Take only those cells that have not been taken before.
if cellpath not in cellUniques:
cell = moose.Cell(cellpath)
cellList.append(cell)
cellUniques.append(cellpath)
cellnum += 1
if not allcells and cellnum == 30: break
else:
if allcells: cellList = network.populationDict[population][1].values()
else: cellList = network.populationDict[population][1].values()[0:60]
return cellList
def exportMitralTables(mitMOOSETables,mitcolours,mitspikes):
mitTables = []
for miti,(mitname,mitCellTables) in enumerate(mitMOOSETables):
mitTables.append((mitname,mitspikes,mitcolours[miti],\
[(compname,array(compTable)) for compname,compTable in mitCellTables]))
return mitTables
def setupMitralTables(network,mitspikes):
mitTables = []
for mit in network.populationDict['mitrals'][1].values():
mitTables.append((mit.name,setupCellTables(mit,mitspikes)))
return mitTables
def setupCellTables(cellObj,spikes):
cellTables = []
for compartmentid in cellObj.getChildren(cellObj.id): # compartments
comp = moose.Compartment(compartmentid)
cellTable = setupTable('VmTableExtra',comp,'Vm')
if spikes:
cellTable.stepMode = TAB_SPIKE
cellTable.stepSize = THRESHOLD
cellTables.append((comp.name,cellTable))
return cellTables
def numpy_convert_tables(tables):
return [[array(table) for table in onetypetables] for onetypetables in tables]
def plot_extras(timevec, tables, nopgs, nosingles, nojoints, nomultis, plottitle=''):
if not nopgs:
figure()
title("PGs "+plottitle)
for pgtable in tables[0]:
plot(timevec[:len(pgtable)], pgtable, ',')
if not nosingles:
figure()
title("single granules "+plottitle)
for singletable in tables[1]:
plot(timevec[:len(singletable)], singletable, ',')
if not nojoints:
figure()
title("joint granules "+plottitle)
for jointtable in tables[2]:
plot(timevec[:len(jointtable)], jointtable, ',')
if not nomultis:
figure()
title("multi granules "+plottitle)
for multitable in tables[3]:
plot(timevec[:len(multitable)], multitable, ',')
def plot_extras_spikes(binvec, tables, nopgs, nosingles,\
nojoints, nomultis, bins, runt, settlet, plottitle=''):
if not nopgs:
figure()
title("PGs "+plottitle)
for pgtable in tables[0]:
plot(binvec, plotBins(pgtable, bins, runt, settlet), '.-')
axes_labels(gca(),"time (s)","rate (Hz)",fontsize=label_fontsize+2)
if not nosingles:
figure()
title("single granules "+plottitle)
for singletable in tables[1]:
plot(binvec, plotBins(singletable, bins, runt, settlet), '.-')
axes_labels(gca(),"time (s)","rate (Hz)",fontsize=label_fontsize+2)
if not nojoints:
figure()
title("joint granules "+plottitle)
for jointtable in tables[2]:
plot(binvec, plotBins(jointtable, bins, runt, settlet), '.-')
axes_labels(gca(),"time (s)","rate (Hz)",fontsize=label_fontsize+2)
if not nomultis:
figure()
title("multi granules "+plottitle)
for multitable in tables[3]:
plot(binvec, plotBins(multitable, bins, runt, settlet), '.-')
axes_labels(gca(),"time (s)","rate (Hz)",fontsize=label_fontsize+2)
def print_extras_activity(tables, nopgs, nosingles, nojoints, nomultis, contextstr):
spikestable = {}
if not nopgs:
numspikes = 0
numcells = 0
for totcells,pgtable in enumerate(tables[0]):
pgtable = array(pgtable)
## MOOSE often inserts one or two spiketime = 0.0 entries
## when storing spikes, so discount those
numspikescell = len(where(pgtable>0.0)[0])
if numspikescell>0:
numspikes += numspikescell
numcells += 1
print "The number of PG cells spiking for",contextstr,"is",\
numcells,"of",totcells+1,"; number of spikes =",numspikes
pgstable = (numcells,totcells,numspikes)
spikestable['PGs'] = pgstable
if not nosingles:
numspikes = 0
numcells = 0
for totcells,singletable in enumerate(tables[1]):
singletable = array(singletable)
## MOOSE often inserts one or two spiketime = 0.0 entries
## when storing spikes, so discount those
numspikescell = len(where(singletable>0.0)[0])
if numspikescell>0:
numspikes += numspikescell
numcells += 1
print "The number of single granule cells spiking for",contextstr,"is",\
numcells,"of",totcells+1,"; number of spikes =",numspikes
singlestable = (numcells,totcells,numspikes)
spikestable['singles'] = singlestable
if not nojoints:
numspikes = 0
numcells = 0
for totcells,jointtable in enumerate(tables[2]):
jointtable = array(jointtable)
## MOOSE often inserts one or two spiketime = 0.0 entries
## when storing spikes, so discount those
numspikescell = len(where(jointtable>0.0)[0])
if numspikescell>0:
numspikes += numspikescell
numcells += 1
print "The number of joint granule cells spiking for",contextstr,"is",\
numcells,"of",totcells+1,"; number of spikes =",numspikes
jointstable = (numcells,totcells,numspikes)
spikestable['joints'] = jointstable
if not nomultis:
numspikes = 0
numcells = 0
for totcells,multitable in enumerate(tables[3]):
multitable = array(multitable)
## MOOSE often inserts one or two spiketime = 0.0 entries
## when storing spikes, so discount those
numspikescell = len(where(multitable>0.0)[0])
if numspikescell>0:
numspikes += numspikescell
numcells += 1
print "The number of multi granule cells spiking for",contextstr,"is",\
numcells,"of",totcells+1,"; number of spikes =",numspikes
multistable = (numcells,totcells,numspikes)
spikestable['multis'] = multistable
return spikestable
## non-overlapping bins
def rebin_pulses(mitral_responses_list, numbins, runtime, settletime):
numtrials = len(mitral_responses_list)
numtrains = len(mitral_responses_list[0])
nummits = len(mitral_responses_list[0][0])
return [[[ plotBins( mitral_responses_list[trialnum][trainnum][mitnum],\
numbins, runtime, settletime) \
for mitnum in range(nummits)] \
for trainnum in range(numtrains)] \
for trialnum in range(numtrials)]
### overlapping bins
#def rebin_pulses(mitral_responses_list, numbins, runtime, settletime, bin_width_time):
# numtrials = len(mitral_responses_list)
# numtrains = len(mitral_responses_list[0])
# nummits = len(mitral_responses_list[0][0])
# return [[[ plotOverlappingBins( mitral_responses_list[trialnum][trainnum][mitnum],\
# numbins, runtime, settletime, bin_width_time ) \
# for mitnum in range(nummits)] \
# for trainnum in range(numtrains)] \
# for trialnum in range(numtrials)]
def rebin(mitral_responses_list, numbins, bin_width_time, numresps=1):
numtrials = len(mitral_responses_list)
numodors = len(mitral_responses_list[0])
nummits = len(mitral_responses_list[0][0])
## NUM_RESPS is number of RESPIRATION cycles simulated, we rebin and return the last 'numresps'
return [[[ plotOverlappingBins( mitral_responses_list[trialnum][odornum][mitnum],\
numbins, RESPIRATION*numresps, SETTLETIME+(NUM_RESPS-numresps)*RESPIRATION, bin_width_time )
for mitnum in range(nummits)] \
for odornum in range(numodors)] \
for trialnum in range(numtrials)]
def build_tweaks(mitralsclub, nospineinh, nosingles,
nojoints, nomultis, nopgs, onlytwomits,
includeProjections=[], twomitrals=(0,2), nolateral=False):
"""
Excludes singles/joints/multis granules and their projections as appropriate
Excludes extra-excitation from unmodeled sisters as appropriate
Include only the two required mitrals (and its connected granules) if ONLY_TWO_MITS is True
"""
excludePopulations = []
excludeProjections = ['SA']
## In odor_pulses, odor_morphs, scaled_pulses, I have not specified to include
## file-based inputs to 2nd order cells as below. If not specified, force include:
if 'granule_baseline' not in includeProjections: includeProjections.append('granule_baseline')
if 'ORN_PG' not in includeProjections: includeProjections.append('ORN_PG')
if not mitralsclub:
excludeProjections.append('mitral_granule_extra_exc')
if nospineinh:
excludeProjections.append('_spinesingles')
excludeProjections.append('_spinejoints')
excludeProjections.append('_spinemultis')
if nosingles:
excludePopulations.append('singles')
excludeProjections.append('_singles') # _ to avoid deleting spinesingles
if nojoints:
excludePopulations.append('joints')
excludeProjections.append('_joints') # _ to avoid deleting spinejoints
if nomultis:
excludePopulations.append('multis')
excludeProjections.append('_multis') # _ to avoid deleting spinemultis
if nopgs:
excludePopulations.append('PGs')
excludeProjections.append('PG')
if onlytwomits:
onlyInclude = {'includePopulation':('mitrals',[str(twomitrals[0]),str(twomitrals[1])]),
'includeProjections':includeProjections}
return {'excludePopulations':excludePopulations,
'excludeProjections':excludeProjections,'onlyInclude':onlyInclude}
else:
if nolateral:
## remove other mitrals so that there is no lateral inhibition
## differs from nojoints, in keeping the joints self-inhibition
print "EXCLUDING OTHER MITS, KEEPING ONLY mits 0 and 1"
onlyInclude = {'includePopulation':('mitrals',['0','1']),
'includeProjections':includeProjections}
return {'excludePopulations':excludePopulations,
'excludeProjections':excludeProjections,'onlyInclude':onlyInclude}
else:
return {'excludePopulations':excludePopulations,\
'excludeProjections':excludeProjections}
